1. Field of the Invention
The present invention relates to an automation method for computerized tomography (CT) image analysis using automated calculation of an evaluation index for quantitatively analyzing a degree of thoracic deformation based on automatic initialization, and a record medium and apparatus, and more particularly, to an automation method for CT image analysis and a record medium and apparatus, which completely automate an initialization operation for image segmentation by applying various image processing techniques, and thus automatically extract the internal boundary information of a thorax from a CT image and automatically calculate indexes for evaluating the degree of thoracic deformation of a funnel chest patient on the basis of the extracted information.
2. Discussion of Related Art
Evaluation preceding surgery for a degree of thoracic deformation of a funnel chest (pectus excavatum) patient is necessary for preparing a surgery, and evaluation succeeding the surgery is important to determine the result of the surgery.
Therefore, in order to establish a surgery plan for curing a funnel chest patient and analyze the result of the surgery before and after, various indexes such as a Haller index, a vertebral index, and a depression index for quantitatively expressing the degree of a thoracic deformation are being used in clinical trials.
Referring to FIG. 1, for example, a Haller index is defined as a/c (i.e., Haller index=a/c), and a vertebral index is defined as 100*v/(v+c) (i.e., vertebral index=100*v/(v+c)). Here, “a” is the left and right length of a thorax, “c” is the distance between a sternum and a spine, and “v” is the length of a spine body.
A depression index, an asymmetry index, an eccentricity index, and an unbalance index are extracted by calculating an angle and a ratio of different lengths on the basis of the thorax information of FIG. 1 depending on the case and are used for a surgery of a funnel chest based on a Nuss surgery scheme.
Typically, the calculation of the indexes depends on a manual measurement scheme of manually analyzing the thoracic CT image of a patient, calculating several measurement values necessary for the calculation of the indexes and calculating the indexes. For this reason, time spent on measuring and calculating is long, and a large deviation of the calculated results occurs according to the measurers and measurement conditions.
Accordingly, it is required to automate the calculation of the indexes and thus shorten time spent on calculating and remove the deviation of the calculated results. Referring to FIG. 2, in the active contour model (ACM), a measurer such as an operating surgeon manually marks ten initial points around a region of interest (ROI), i.e., a thoracic boundary, in order to extract a boundary value of the ROI, and the marked points are interconnected by an interpolation scheme, thereby generating an initial contour line 210.
Furthermore, by performing a deformation operation of the ACM, that is, an image segmentation algorithm, a thorax internal boundary 220 is finally extracted from the initial contour line 210. The indexes that represent the degree of thoracic deformation are calculated with the extracted thorax internal boundary 220.
However, even in such a CT image analysis scheme, there is a limitation in that a measurer manually marks the initial points for image segmentation for a thoracic boundary, and thus finds the initial contour line 210 and extracts the thorax internal boundary 220. Also, a clinical doctor needs to understand relevant engineering technology and perform a practice operation to master the engineering technology in order to set accurate initial points.